code: def init_lambada_model(
      model_dir,
      threshold,
      min_length,
      max_length,
      batch_size,
      temperature,
      top_k,
      top_p,
      repetition_penalty,
      device,
      force_reload,
  ):
      global LAMBADA_MODELS
  
      model_name = "_".join([os.path.basename(model_dir), str(device)])
      if model_name in LAMBADA_MODELS and not force_reload:
          LAMBADA_MODELS[model_name].threshold = threshold
          LAMBADA_MODELS[model_name].min_length = min_length
          LAMBADA_MODELS[model_name].max_length = max_length
          LAMBADA_MODELS[model_name].batch_size = batch_size
          LAMBADA_MODELS[model_name].temperature = temperature
          LAMBADA_MODELS[model_name].top_k = top_k
          LAMBADA_MODELS[model_name].top_p = top_p
          LAMBADA_MODELS[
              model_name
          ].repetition_penalty = repetition_penalty
          return LAMBADA_MODELS[model_name]
  
      model = nml.Lambada(
          cls_model_dir=os.path.join(model_dir, "cls"),
          gen_model_dir=os.path.join(model_dir, "gen"),
          threshold=threshold,
          max_length=max_length,
          min_length=min_length,
          temperature=temperature,
          top_k=top_k,
          top_p=top_p,
          repetition_penalty=repetition_penalty,
          batch_size=batch_size,
          device=device,
      )
  
      LAMBADA_MODELS[model_name] = model
      return model
  
file_path: nlpaug/nlpaug/augmenter/sentence/lambada.py
imports: import os
  import json
  from nlpaug.augmenter.sentence import SentenceAugmenter
  import nlpaug.model.lang_models
  from nlpaug.util import Action, Doc
label_desc: Use image node_id 1 for calling a global function with example usage: init_lambada_model(model_dir, threshold, min_length, max_length, batch_size, temperature, top_k, top_p, repetition_penalty, device, force_reload) and returns: model, LAMBADA_MODELS